Prosper Loan Data Exploration

by Puneet Sharma

Preliminary Wrangling

This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others..

In [1]:
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb

%matplotlib inline

Loading dataset.

In [2]:
df = pd.read_csv('prosperLoanData.csv')
In [3]:
df.ListingCreationDate = pd.to_datetime(df.ListingCreationDate)
In [4]:
df.ClosedDate = pd.to_datetime(df.ClosedDate)
In [5]:
df.loc[df.EmploymentStatus == 'Not available', 'EmploymentStatus'] = np.nan
In [6]:
df.loc[df.BorrowerAPR.isna(), 'BorrowerAPR'] = np.nan
In [7]:
df.DateCreditPulled = pd.to_datetime(df.DateCreditPulled)
In [8]:
df.FirstRecordedCreditLine = pd.to_datetime(df.FirstRecordedCreditLine)
In [9]:
df.LoanOriginationDate = pd.to_datetime(df.LoanOriginationDate)

Feature Engineering

In [21]:
df['ListingCreationYear'] = df.ListingCreationDate.apply(lambda x: int(str(x).split()[0].split('-')[0]))
In [33]:
df_loan_amt_each_year = df.groupby(['ListingCategory (numeric)', 'ListingCreationYear']).LoanOriginalAmount.sum().reset_index(name="sum")
df_loan_amt_each_year.head()
Out[33]:
ListingCategory (numeric) ListingCreationYear sum
0 0 2005 88687
1 0 2006 30100789
2 0 2007 75795140
3 0 2011 44505
4 0 2012 16000
In [34]:
df_loan_amt_each_year_2 = df_loan_amt_each_year.groupby('ListingCreationYear')['sum'].sum().reset_index(name="sum")
df_loan_amt_each_year_2
Out[34]:
ListingCreationYear sum
0 2005 88687
1 2006 30100789
2 2007 81670928
3 2008 67180398
4 2009 9692013
5 2010 26775582
6 2011 76711783
7 2012 154033169
8 2013 375740433
9 2014 127900565
In [48]:
df['LoanGoodBad'] = df.LoanStatus.apply(lambda x: 1 if x in ['Completed', 'Current', 'FinalPaymentInProgress'] else 0)
In [47]:
df['CreditHistory'] = ((df.DateCreditPulled - df.FirstRecordedCreditLine)/np.timedelta64(1, 'M'))
In [49]:
df.loc[df.CreditHistory.notnull(), 'CreditHistory'] = df[df.CreditHistory.notnull()].CreditHistory.astype(int)
In [51]:
df['IsBorrowerHomeownerNum'] = df.IsBorrowerHomeowner.apply(lambda x: 1 if x else 0)
In [52]:
df_numeric = df[['BorrowerAPR', 'BorrowerRate', 'LenderYield', 'EmploymentStatusDuration', 'IsBorrowerHomeownerNum', 'CreditScoreRangeLower', 'CreditScoreRangeUpper', 'CurrentCreditLines', 'OpenCreditLines', 'TotalCreditLinespast7years', 'OpenRevolvingAccounts', 'OpenRevolvingMonthlyPayment', 'InquiriesLast6Months', 'TotalInquiries', 'CurrentDelinquencies', 'AmountDelinquent', 'RevolvingCreditBalance', 'BankcardUtilization', 'AvailableBankcardCredit', 'DebtToIncomeRatio', 'LoanMonthsSinceOrigination', 'LoanOriginalAmount', 'MonthlyLoanPayment', 'LP_CustomerPayments', 'LP_CustomerPrincipalPayments', 'LP_InterestandFees', 'LP_ServiceFees', 'LP_CollectionFees', 'LP_GrossPrincipalLoss', 'LP_NetPrincipalLoss', 'LP_NonPrincipalRecoverypayments', 'PercentFunded', 'Recommendations', 'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount', 'Investors', 'CreditHistory', 'LoanGoodBad']]
In [53]:
def check_income(val):
    if val in ['Not displayed', 'Not employed', '$0']:
        return 0
    elif val in ['$25,000-49,999', '$1-24,999']:
        return 1
    else:
        return 2
In [54]:
df_numeric['IncomeRangeCat'] = df.IncomeRange.apply(check_income)
/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.
In [66]:
df['IncomeRangeCat'] = df.IncomeRange.apply(check_income)
In [56]:
dummy = pd.get_dummies(df.LoanStatus)
In [57]:
df_numeric = pd.concat([df_numeric, dummy], axis=1)
In [58]:
df_numeric.head()
Out[58]:
BorrowerAPR BorrowerRate LenderYield EmploymentStatusDuration IsBorrowerHomeownerNum CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines TotalCreditLinespast7years ... Completed Current Defaulted FinalPaymentInProgress Past Due (1-15 days) Past Due (16-30 days) Past Due (31-60 days) Past Due (61-90 days) Past Due (91-120 days) Past Due (>120 days)
0 0.16516 0.1580 0.1380 2.0 1 640.0 659.0 5.0 4.0 12.0 ... 1 0 0 0 0 0 0 0 0 0
1 0.12016 0.0920 0.0820 44.0 0 680.0 699.0 14.0 14.0 29.0 ... 0 1 0 0 0 0 0 0 0 0
2 0.28269 0.2750 0.2400 NaN 0 480.0 499.0 NaN NaN 3.0 ... 1 0 0 0 0 0 0 0 0 0
3 0.12528 0.0974 0.0874 113.0 1 800.0 819.0 5.0 5.0 29.0 ... 0 1 0 0 0 0 0 0 0 0
4 0.24614 0.2085 0.1985 44.0 1 680.0 699.0 19.0 19.0 49.0 ... 0 1 0 0 0 0 0 0 0 0

5 rows × 51 columns

In [59]:
corr = df_numeric.corr()

What is the structure of your dataset?

113937, 85

What is/are the main feature(s) of interest in your dataset?

['LoanStatus', 'BorrowerAPR', 'BorrowerRate', 'LenderYield', 'ListingCategory (numeric)', 'Occupation', 'EmploymentStatus', 'EmploymentStatusDuration', 'IsBorrowerHomeowner', 'CreditScoreRangeLower', 'CreditScoreRangeUpper', 'FirstRecordedCreditLine', 'DateCreditPulled', 'CurrentCreditLines', 'OpenCreditLines', 'TotalCreditLinespast7years', 'OpenRevolvingAccounts', 'OpenRevolvingMonthlyPayment', 'InquiriesLast6Months', 'TotalInquiries', 'CurrentDelinquencies', 'AmountDelinquent', 'RevolvingCreditBalance', 'BankcardUtilization', 'AvailableBankcardCredit', 'DebtToIncomeRatio', 'IncomeRange', 'LoanMonthsSinceOrigination', 'LoanOriginalAmount', 'MonthlyLoanPayment', 'LP_CustomerPayments', 'LP_CustomerPrincipalPayments', 'LP_InterestandFees', 'LP_ServiceFees', 'LP_CollectionFees', 'LP_GrossPrincipalLoss', 'LP_NetPrincipalLoss', 'LP_NonPrincipalRecoverypayments', 'PercentFunded', 'Recommendations', 'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount', 'Investors']

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

['LoanStatus', 'BorrowerRate', 'CreditScoreRangeUpper', 'TotalInquiries', 'BankcardUtilization', 'LoanOriginalAmount', 'Investors', 'IncomeRangeCat', 'LoanGoodBad']

Univariate Exploration

In this section, investigate distributions of individual variables. If you see unusual points or outliers, take a deeper look to clean things up and prepare yourself to look at relationships between variables.

In [10]:
base_color = sb.color_palette()[0]
In [11]:
sb.countplot(x="CreditGrade", color=base_color, data=df);

Above distribution shows how "Credit Grade" is distributed. 'C' Grade is given highest.

In [12]:
sb.countplot(x='Term', color=base_color, data=df);

3 Years loan is most popular.

In [13]:
plt.figure(figsize=[10, 10])
sb.countplot(y='LoanStatus', color=base_color, data=df)
plt.xscale('log')
plt.xticks([10, 100, 300, 1000, 3000, 10000, 50000], ['10', '100', '300', '1K', '3K', '10K', '50K']);

How Loan Status is distributed. Total count of each type of Loan Status.

In [25]:
bin_edges = np.arange(0, df.BorrowerRate.max()+0.01, 0.01)
sb.distplot(df.BorrowerRate, bins=bin_edges, kde=False, hist_kws={'alpha': 1});

Intrest Rate distributtion.

In [26]:
bin_edges = np.arange(-0.1, df.LenderYield.max()+0.01, 0.01)
sb.distplot(df.LenderYield, bins=bin_edges, kde=False, hist_kws={'alpha': 1});

Watch how close match is Lender Yield with Interest rate.

In [22]:
sb.countplot(x="ListingCreationYear", color=base_color, data=df);

How many loans distributed each year.

In [27]:
base_color = sb.color_palette()[0]
plt.figure(figsize=[10, 8])
sb.countplot(data=df, y='ListingCategory (numeric)', color=base_color);
y_tick_num = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
y_tick = ['Not Available', 'Debt Consolidation', 'Home Improvement', 'Business', 'Personal Loan', 'Student Use', 'Auto', 'Other', 'Baby&Adoption', 'Boat', 'Cosmetic Procedure', 'Engagement Ring', 'Green Loans', 'Household Expenses', 'Large Purchases', 'Medical/Dental', 'Motorcycle', 'RV', 'Taxes', 'Vacation', 'Wedding Loans']
plt.xscale('log')
tick_locs = [1, 10, 100, 300, 1000, 3000, 10000, 50000]
plt.xticks(tick_locs, tick_locs)
plt.yticks(y_tick_num, y_tick);

How many loans are distributed for different category. 'Debt Consolidation' is what people take most loan against.

In [44]:
plt.figure(figsize=[20, 10])
sb.countplot(x="Occupation", color=base_color, order=df.Occupation.value_counts().index, data=df)
plt.yscale('log')
tick_locs = [10, 50, 100, 500, 1000, 5000, 10000, 20000, 30000]
tick_locs_t = ['10', '50', '100', '500', '1K', '5K', '10K', '20K', '30K']
plt.yticks(tick_locs, tick_locs_t)
plt.xticks(rotation=90);

Which profession takes most loans

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?

  1. Most loans are distributed in 2013.
  2. Interest Rate looks uniformly distributed expect two peaks at the end.
  3. Most loans are in 'current' or 'completed' state which is good sign for lender. But count of 'chargedoff' loans also higher.
  4. Did log transformation to get more clear picture of types of loans and which people profession loans disribution.

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

There are lot of categories for loan status. Created new column to shink it to two category to get better correlation. Also, changed income category. Created new columns "CreditHistory" and "LoanYear" to get better grouping.

Bivariate Exploration

In this section, investigate relationships between pairs of variables in your data. Make sure the variables that you cover here have been introduced in some fashion in the previous section (univariate exploration).

In [14]:
plt.figure(figsize=[10, 10])
sb.countplot(y='LoanStatus', hue='Term', data=df)
plt.xscale('log')
plt.xticks([10, 100, 300, 1000, 3000, 10000, 50000], ['10', '100', '300', '1K', '3K', '10K', '50K']);

Adding loan duration to loan status. Clearly because there are lot of 3Year loans so more variations in status.

In [23]:
sb.countplot(x="ListingCreationYear", hue='Term', data=df);

How many loans of different duration are provided each year.

In [30]:
base_color = sb.color_palette()[0]
plt.figure(figsize=[8, 6])
sb.countplot(data=df, y='ListingCategory (numeric)', hue='Term');
x_tick_num = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
x_tick = ['Not Available', 'Debt Consolidation', 'Home Improvement', 'Business', 'Personal Loan', 'Student Use', 'Auto', 'Other', 'Baby&Adoption', 'Boat', 'Cosmetic Procedure', 'Engagement Ring', 'Green Loans', 'Household Expenses', 'Large Purchases', 'Medical/Dental', 'Motorcycle', 'RV', 'Taxes', 'Vacation', 'Wedding Loans']
plt.yticks(x_tick_num, x_tick);
In [31]:
plt.figure(figsize=[8, 6])
sb.countplot(data=df[df['ListingCategory (numeric)']>1], y='ListingCategory (numeric)', hue='Term');
x_tick_num = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
x_tick = ['Home Improvement', 'Business', 'Personal Loan', 'Student Use', 'Auto', 'Other', 'Baby&Adoption', 'Boat', 'Cosmetic Procedure', 'Engagement Ring', 'Green Loans', 'Household Expenses', 'Large Purchases', 'Medical/Dental', 'Motorcycle', 'RV', 'Taxes', 'Vacation', 'Wedding Loans']
plt.yticks(x_tick_num, x_tick);
In [32]:
plt.figure(figsize=[8, 6])
sb.countplot(data=df, y='ListingCategory (numeric)', hue='Term');
y_tick_num = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
y_tick = ['Not Available', 'Debt Consolidation', 'Home Improvement', 'Business', 'Personal Loan', 'Student Use', 'Auto', 'Other', 'Baby&Adoption', 'Boat', 'Cosmetic Procedure', 'Engagement Ring', 'Green Loans', 'Household Expenses', 'Large Purchases', 'Medical/Dental', 'Motorcycle', 'RV', 'Taxes', 'Vacation', 'Wedding Loans']
plt.xscale('log')
tick_locs = [1, 10, 100, 300, 1000, 3000, 10000]
plt.xticks(tick_locs, tick_locs)
plt.yticks(y_tick_num, y_tick);

Which type of loan is popular and for what duration.

In [40]:
sb.barplot(data=df_loan_amt_each_year_2, x = 'ListingCreationYear', y='sum', color=base_color)
tick_locs = [10000000, 50000000, 100000000, 150000000, 250000000, 350000000]
tick_locs_t = ['$10M', '$50M', '$100M', '$150M', '$250M', '$350M']
plt.yticks(tick_locs, tick_locs_t);
In [41]:
base_color = sb.color_palette()[0]
sb.barplot(data=df_loan_amt_each_year_2, x = 'ListingCreationYear', y='sum', color=base_color)
plt.yscale('log')
tick_locs = [100000, 1000000, 10000000, 30000000, 100000000, 400000000]
tick_locs_t = ['$100K', '$1M', '$10M', '$30M', '$100M', '$400M']
plt.yticks(tick_locs, tick_locs_t);

Total how much loan given each year.

In [45]:
g = sb.FacetGrid(data = df, col='LoanStatus', col_wrap=2, height=10)
g.map(sb.countplot, 'Occupation', orient='h', order=df.Occupation.value_counts().index)
g.set(yscale='log')
y_ticks = [1, 5, 10, 50, 100, 500, 1000, 5000, 10000]
g.set(yticks=y_ticks, yticklabels=y_ticks)
for ax in g.axes.flat:
    for label in ax.get_xticklabels():
        label.set_rotation(90)
In [46]:
plt.figure(figsize=[10, 20])
sb.countplot(y="Occupation", hue="LoanGoodBad", order=df.Occupation.value_counts().index, data=df)
plt.xscale('log')
tick_locs = [1, 5, 10, 50, 100, 500, 1000, 5000, 10000]
plt.xticks(tick_locs, tick_locs);

Which profession have good/bad loans. Good being in 'current', 'completed' or 'final' stage.

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

All Canceled loans are 3Yr. Most Bad loans are 3Yr or 5Yr. But that might be because of the fact that 3Yr and 5Yr loans are distributed most. Most loans are taken for 'Debt Consolidation' Loans given to Dentist and Judge are safest. Don't get Defaulted or Chargedoff

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

'Student Use' loans are decreasing over time.

Multivariate Exploration

Create plots of three or more variables to investigate your data even further. Make sure that your investigations are justified, and follow from your work in the previous sections.

In [19]:
fig, ax = plt.subplots(figsize = (10,8))
ax.scatter(df.ListingCreationDate.values, df.BorrowerRate.values, alpha=0.1, c=df.Term);
In [20]:
plt.figure(figsize=[10, 8])
sb.scatterplot(x="ListingCreationDate", y="BorrowerRate", hue='Term', alpha=0.1, data=df)
plt.xlim('2005-06-01', '2014-06-01');

How interest rate is distributed and how 1Yr 3Yr and 5Yr loans are provided. Clearly interest rates were very high for some loans to begin with and 5Yr loans are started later.

In [38]:
plt.figure(figsize=[20, 8])
sb.barplot(x="ListingCreationYear", y='sum', hue='ListingCategory (numeric)', data=df_loan_amt_each_year)
plt.yscale('log')
tick_locs = [80000, 100000, 1000000, 10000000, 100000000, 200000000, 400000000]
plt.yticks(tick_locs, tick_locs);
In [39]:
times = df_loan_amt_each_year['ListingCategory (numeric)'].unique()
g = sb.FacetGrid(data = df_loan_amt_each_year, col='ListingCreationYear', col_wrap=3, height=5)
g.map(sb.barplot, 'sum', 'ListingCategory (numeric)', orient="h", order=times)

plt.xscale('log')
tick_locs = [100000, 1000000, 10000000, 30000000, 100000000, 400000000]
tick_locs_t = ['$100K', '$1M', '$10M', '$30M', '$100M', '$400M']
plt.xticks(tick_locs, tick_locs_t)

y_tick_num = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
y_tick = ['Not Available', 'Debt Consolidation', 'Home Improvement', 'Business', 'Personal Loan', 'Student Use', 'Auto', 'Other', 'Baby&Adoption', 'Boat', 'Cosmetic Procedure', 'Engagement Ring', 'Green Loans', 'Household Expenses', 'Large Purchases', 'Medical/Dental', 'Motorcycle', 'RV', 'Taxes', 'Vacation', 'Wedding Loans']
plt.yticks(y_tick_num, y_tick);

How much loan provided each year in each category.

In [60]:
corr.style.background_gradient().set_precision(3)
Out[60]:
BorrowerAPR BorrowerRate LenderYield EmploymentStatusDuration IsBorrowerHomeownerNum CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines TotalCreditLinespast7years OpenRevolvingAccounts OpenRevolvingMonthlyPayment InquiriesLast6Months TotalInquiries CurrentDelinquencies AmountDelinquent RevolvingCreditBalance BankcardUtilization AvailableBankcardCredit DebtToIncomeRatio LoanMonthsSinceOrigination LoanOriginalAmount MonthlyLoanPayment LP_CustomerPayments LP_CustomerPrincipalPayments LP_InterestandFees LP_ServiceFees LP_CollectionFees LP_GrossPrincipalLoss LP_NetPrincipalLoss LP_NonPrincipalRecoverypayments PercentFunded Recommendations InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors CreditHistory LoanGoodBad IncomeRangeCat Cancelled Chargedoff Completed Current Defaulted FinalPaymentInProgress Past Due (1-15 days) Past Due (16-30 days) Past Due (31-60 days) Past Due (61-90 days) Past Due (91-120 days) Past Due (>120 days)
BorrowerAPR 1 0.99 0.989 -0.00859 -0.133 -0.43 -0.43 -0.0935 -0.0989 0.00251 -0.11 -0.0434 0.146 0.115 0.149 0.0657 -0.0585 0.261 -0.349 0.0563 -0.0738 -0.323 -0.227 -0.111 -0.18 0.17 0.117 -0.0415 0.0673 0.0674 0.0285 -0.0282 -0.0442 -0.0468 -0.0318 -0.308 -0.0289 -0.195 -0.0861 -0.00107 0.166 -0.0885 -0.0629 0.0537 0.00567 0.0473 0.0298 0.033 0.0356 0.0335 0.00961
BorrowerRate 0.99 1 0.999 -0.0199 -0.134 -0.462 -0.462 -0.0975 -0.106 -0.00579 -0.126 -0.0556 0.184 0.153 0.177 0.0656 -0.0596 0.255 -0.344 0.0629 0.0204 -0.329 -0.245 -0.0811 -0.154 0.2 0.096 -0.0534 0.101 0.1 0.0421 -0.029 -0.0306 -0.0373 -0.0265 -0.274 -0.0531 -0.235 -0.128 -0.000741 0.195 -0.06 -0.119 0.087 0.00238 0.0429 0.0274 0.0304 0.0331 0.0315 0.0095
LenderYield 0.989 0.999 1 -0.019 -0.133 -0.454 -0.454 -0.0963 -0.105 -0.00547 -0.125 -0.0547 0.18 0.15 0.172 0.065 -0.0587 0.254 -0.341 0.0619 0.0192 -0.328 -0.244 -0.0791 -0.152 0.201 0.0979 -0.053 0.0995 0.0992 0.041 -0.0292 -0.0311 -0.0373 -0.0266 -0.274 -0.0521 -0.232 -0.129 -0.000382 0.193 -0.058 -0.119 0.0847 0.00243 0.0432 0.0276 0.0306 0.0333 0.0316 0.00954
EmploymentStatusDuration -0.00859 -0.0199 -0.019 1 0.171 0.0811 0.0811 0.14 0.14 0.172 0.155 0.179 -0.048 -0.0447 -0.00908 0.00818 0.118 0.0823 0.053 -0.0116 -0.172 0.0981 0.0812 -0.0393 -0.0453 -0.0031 0.000201 0.00995 -0.0362 -0.0357 -0.00479 0.000998 -0.0394 -0.0314 -0.0208 -0.0416 0.32 0.062 0.178 -0.00214 -0.0561 -0.106 0.143 -0.0314 0.000688 0.00387 -0.00256 -0.00159 0.00336 -0.0044 0.00398
IsBorrowerHomeownerNum -0.133 -0.134 -0.133 0.171 1 0.294 0.294 0.278 0.279 0.294 0.189 0.271 0.00689 0.0666 -0.0555 0.0381 0.225 0.0866 0.142 0.000177 -0.0862 0.207 0.18 0.0946 0.0892 0.0762 -0.124 -0.00106 0.0243 0.0247 0.00532 -0.0057 -0.00958 -0.0104 -0.00761 0.125 0.201 0.0478 0.253 -0.00138 -0.0411 -0.0345 0.068 -0.022 0.00273 -0.0062 -0.00207 0.000273 0.00708 -0.00897 -0.000106
CreditScoreRangeLower -0.43 -0.462 -0.454 0.0811 0.294 1 1 0.142 0.148 0.109 0.211 0.139 -0.262 -0.277 -0.368 -0.0658 0.0888 -0.405 0.453 -0.0132 -0.333 0.341 0.293 0.21 0.225 0.0768 -0.238 0.0312 -0.0611 -0.0607 -0.0416 -0.0304 -0.0351 -0.0132 -0.023 0.271 0.225 0.267 0.309 -0.0081 -0.189 0.000206 0.198 -0.207 0.00949 0.00255 -0.00246 0.00503 0.00252 0.00361 0.000345
CreditScoreRangeUpper -0.43 -0.462 -0.454 0.0811 0.294 1 1 0.142 0.148 0.109 0.211 0.139 -0.262 -0.277 -0.368 -0.0658 0.0888 -0.405 0.453 -0.0132 -0.333 0.341 0.293 0.21 0.225 0.0768 -0.238 0.0312 -0.0611 -0.0607 -0.0416 -0.0304 -0.0351 -0.0132 -0.023 0.271 0.225 0.267 0.309 -0.0081 -0.189 0.000206 0.198 -0.207 0.00949 0.00255 -0.00246 0.00503 0.00252 0.00361 0.000345
CurrentCreditLines -0.0935 -0.0975 -0.0963 0.14 0.278 0.142 0.142 1 0.96 0.628 0.853 0.545 0.0697 0.151 -0.166 -0.0726 0.334 0.117 0.318 0.0919 -0.121 0.201 0.187 0.0222 0.0186 0.0256 -0.0488 0.016 -0.00654 -0.00588 -0.00201 0.00807 -0.0113 -0.0133 -0.00903 0.0394 0.209 0.0748 0.254 -0.00074 -0.0899 -0.0778 0.126 0.0107 -0.00059 -0.00633 0.00204 -0.0081 -0.00736 -0.00495 -0.00422
OpenCreditLines -0.0989 -0.106 -0.105 0.14 0.279 0.148 0.148 0.96 1 0.587 0.885 0.565 0.035 0.121 -0.162 -0.0704 0.343 0.123 0.332 0.0902 -0.169 0.224 0.205 0.00347 -0.00048 0.0156 -0.0434 0.0257 -0.019 -0.0178 -0.0109 0.00698 -0.0211 -0.0186 -0.0101 0.0252 0.207 0.0914 0.254 -0.00138 -0.102 -0.11 0.167 -0.00329 0.000541 -0.00867 0.00304 -0.00663 -0.00548 -0.00442 -0.00415
TotalCreditLinespast7years 0.00251 -0.00579 -0.00547 0.172 0.294 0.109 0.109 0.628 0.587 1 0.519 0.365 0.0726 0.168 0.0676 0.051 0.238 0.101 0.194 0.0375 -0.134 0.145 0.135 0.0175 0.0105 0.0348 -0.0415 -0.00164 -0.00381 -0.00369 0.0119 -0.00178 -0.00731 -0.0161 -0.00684 0.00823 0.368 0.0486 0.257 -0.00273 -0.0546 -0.0707 0.103 -0.00983 0.00214 0.00511 0.00615 0.00252 -0.000643 -0.00234 -0.0042
OpenRevolvingAccounts -0.11 -0.126 -0.125 0.155 0.189 0.211 0.211 0.853 0.885 0.519 1 0.585 -0.0198 0.0444 -0.192 -0.0604 0.336 0.0768 0.377 0.0758 -0.213 0.233 0.217 0.0221 0.0191 0.0239 -0.0687 0.0273 -0.0309 -0.03 -0.0138 -0.000285 -0.0115 -0.00848 -0.00523 0.0498 0.259 0.122 0.241 -0.00854 -0.103 -0.103 0.189 -0.0614 0.003 -0.00836 0.00248 -0.00626 -0.00436 -0.00139 -0.00728
OpenRevolvingMonthlyPayment -0.0434 -0.0556 -0.0547 0.179 0.271 0.139 0.139 0.545 0.565 0.365 0.585 1 -0.0483 -0.0193 -0.161 -0.0515 0.761 0.298 0.253 0.0866 -0.168 0.266 0.261 0.0557 0.0395 0.0895 -0.108 0.00663 0.0162 0.0164 0.00361 -0.00524 -0.0215 -0.0122 0.00281 0.0555 0.302 0.0738 0.268 -0.00557 -0.0688 -0.111 0.16 -0.0257 0.00137 -0.00593 0.00326 -0.00581 -0.0037 -0.00348 -0.00631
InquiriesLast6Months 0.146 0.184 0.18 -0.048 0.00689 -0.262 -0.262 0.0697 0.035 0.0726 -0.0198 -0.0483 1 0.742 0.156 0.024 -0.0074 -0.0326 -0.00456 0.0244 0.333 -0.103 -0.0638 0.0177 0.0127 0.0282 0.0551 -0.0502 0.189 0.189 0.0631 0.012 0.0443 0.0236 0.0227 0.0135 -0.101 -0.248 -0.122 0.00503 0.187 0.0573 -0.238 0.183 -0.00556 -0.0106 -0.00595 -0.00391 -0.00574 -0.00737 -0.000598
TotalInquiries 0.115 0.153 0.15 -0.0447 0.0666 -0.277 -0.277 0.151 0.121 0.168 0.0444 -0.0193 0.742 1 0.175 0.0315 0.00671 0.0179 -0.0169 0.0286 0.333 -0.0783 -0.0443 0.0159 0.0117 0.024 0.0499 -0.0472 0.187 0.187 0.066 0.0194 0.0548 0.0291 0.0234 0.0263 -0.102 -0.236 -0.0777 0.00112 0.178 0.0574 -0.229 0.18 -0.00352 -0.0135 -0.00703 -0.00648 -0.00888 -0.00866 -0.00375
CurrentDelinquencies 0.149 0.177 0.172 -0.00908 -0.0555 -0.368 -0.368 -0.166 -0.162 0.0676 -0.192 -0.161 0.156 0.175 1 0.341 -0.0888 -0.0438 -0.0924 -0.0243 0.248 -0.172 -0.159 -0.0769 -0.0782 -0.0425 0.0962 -0.0152 0.0513 0.0515 0.0204 0.00513 0.0225 0.0133 0.0153 -0.0871 -0.0222 -0.202 -0.19 0.00723 0.137 0.00134 -0.151 0.165 -0.00455 -0.00436 0.000841 -0.000941 0.00218 -0.0044 -0.000929
AmountDelinquent 0.0657 0.0656 0.065 0.00818 0.0381 -0.0658 -0.0658 -0.0726 -0.0704 0.051 -0.0604 -0.0515 0.024 0.0315 0.341 1 -0.0219 -0.0243 -0.0203 -0.0194 0.0132 -0.0388 -0.0296 -0.0153 -0.0187 0.00248 0.016 -0.00215 0.00396 0.00442 -0.000823 -0.00569 0.0161 0.00714 0.00564 -0.027 0.0429 -0.0258 0.0164 -0.000422 0.0237 -0.00845 -0.0107 0.00556 0.000759 0.000957 0.00398 0.0103 0.00669 -0.00191 0.00101
RevolvingCreditBalance -0.0585 -0.0596 -0.0587 0.118 0.225 0.0888 0.0888 0.334 0.343 0.238 0.336 0.761 -0.0074 0.00671 -0.0888 -0.0219 1 0.246 0.24 0.0386 -0.0417 0.191 0.193 0.08 0.0708 0.0791 -0.0933 0.00108 0.032 0.0324 0.00641 -0.00793 -0.00513 -0.000434 0.0129 0.0722 0.214 0.0266 0.191 -0.00157 -0.0364 -0.0383 0.0543 0.0162 0.00467 -0.00653 0.00243 -0.00568 -0.00401 -0.00679 -0.00393
BankcardUtilization 0.261 0.255 0.254 0.0823 0.0866 -0.405 -0.405 0.117 0.123 0.101 0.0768 0.298 -0.0326 0.0179 -0.0438 -0.0243 0.246 1 -0.351 0.0356 -0.0635 -0.034 -0.0154 -0.104 -0.127 0.0163 0.0594 0.000646 -0.0225 -0.0217 0.0053 0.0144 -0.0161 -0.0219 -0.00456 -0.158 0.0798 -0.00698 0.0782 -0.00493 -0.0102 -0.0976 0.0859 0.0292 4.84e-05 0.00317 0.00252 -0.000307 0.00292 0.000605 -0.0048
AvailableBankcardCredit -0.349 -0.344 -0.341 0.053 0.142 0.453 0.453 0.318 0.332 0.194 0.377 0.253 -0.00456 -0.0169 -0.0924 -0.0203 0.24 -0.351 1 0.00206 -0.0267 0.23 0.196 0.128 0.152 -0.007 -0.1 0.0178 -0.00603 -0.00574 -0.0144 -0.00994 0.0205 0.0279 0.0049 0.208 0.155 0.0822 0.145 4.48e-05 -0.0726 0.0403 0.0212 -0.03 0.00143 -0.0163 -0.00452 -0.0018 -0.00703 -0.00427 -0.0042
DebtToIncomeRatio 0.0563 0.0629 0.0619 -0.0116 0.000177 -0.0132 -0.0132 0.0919 0.0902 0.0375 0.0758 0.0866 0.0244 0.0286 -0.0243 -0.0194 0.0386 0.0356 0.00206 1 0.0454 0.0101 0.0276 0.0212 0.0113 0.0478 -0.0123 -0.0116 0.0724 0.0728 0.0112 0.00232 0.0331 0.0342 0.0279 0.00409 -0.00016 -0.0536 -0.104 -0.00139 0.0386 -0.0152 -0.0251 0.0368 -0.00375 -0.00171 0.0055 -0.00205 0.0093 0.00148 3.99e-05
LoanMonthsSinceOrigination -0.0738 0.0204 0.0192 -0.172 -0.0862 -0.333 -0.333 -0.121 -0.169 -0.134 -0.213 -0.168 0.333 0.333 0.248 0.0132 -0.0417 -0.0635 -0.0267 0.0454 1 -0.309 -0.257 0.296 0.302 0.158 -0.0889 -0.105 0.251 0.247 0.108 0.0131 0.147 0.11 0.0653 0.259 -0.284 -0.355 -0.452 0.0125 0.273 0.511 -0.745 0.283 -0.0232 -0.0442 -0.0253 -0.0303 -0.0267 -0.0243 -0.00517
LoanOriginalAmount -0.323 -0.329 -0.328 0.0981 0.207 0.341 0.341 0.201 0.224 0.145 0.233 0.266 -0.103 -0.0783 -0.172 -0.0388 0.191 -0.034 0.23 0.0101 -0.309 1 0.932 0.365 0.323 0.366 -0.483 -0.0262 0.127 0.127 0.0243 -0.0102 -0.0182 -0.00751 0.0204 0.38 0.166 0.123 0.339 -0.00704 -0.106 -0.244 0.322 -0.0636 6.19e-05 0.00178 -0.0014 0.00179 -0.0051 -0.00276 -0.000106
MonthlyLoanPayment -0.227 -0.245 -0.244 0.0812 0.18 0.293 0.293 0.187 0.205 0.135 0.217 0.261 -0.0638 -0.0443 -0.159 -0.0296 0.193 -0.0154 0.196 0.0276 -0.257 0.932 1 0.425 0.387 0.39 -0.462 -0.0394 0.182 0.183 0.0418 -0.00845 -0.00998 -0.00286 0.0267 0.387 0.144 0.0774 0.313 -0.00725 -0.0661 -0.197 0.244 -0.0434 0.00573 0.00556 0.00112 0.00267 -0.00393 -0.00243 0.000552
LP_CustomerPayments -0.111 -0.0811 -0.0791 -0.0393 0.0946 0.21 0.21 0.0222 0.00347 0.0175 0.0221 0.0557 0.0177 0.0159 -0.0769 -0.0153 0.08 -0.104 0.128 0.0212 0.296 0.365 0.425 1 0.977 0.687 -0.703 -0.0548 -0.0266 -0.028 0.0013 -0.0514 0.0808 0.0755 0.0748 0.563 -0.0395 0.12 0.0527 -0.00578 -0.0927 0.464 -0.349 -0.0706 0.008 -0.00293 -0.00443 -0.00845 -0.00942 -0.00778 -0.00101
LP_CustomerPrincipalPayments -0.18 -0.154 -0.152 -0.0453 0.0892 0.225 0.225 0.0186 -0.00048 0.0105 0.0191 0.0395 0.0127 0.0117 -0.0782 -0.0187 0.0708 -0.127 0.152 0.0113 0.302 0.323 0.387 0.977 1 0.518 -0.577 -0.0257 -0.065 -0.0656 -0.0138 -0.0411 0.0846 0.0808 0.0751 0.551 -0.0503 0.151 0.0433 -0.00506 -0.116 0.536 -0.393 -0.0768 0.00782 -0.0196 -0.0132 -0.0178 -0.0183 -0.0164 -0.00368
LP_InterestandFees 0.17 0.2 0.201 -0.0031 0.0762 0.0768 0.0768 0.0256 0.0156 0.0348 0.0239 0.0895 0.0282 0.024 -0.0425 0.00248 0.0791 0.0163 -0.007 0.0478 0.158 0.366 0.39 0.687 0.518 1 -0.863 -0.134 0.116 0.112 0.0526 -0.0669 0.0361 0.0279 0.0445 0.382 0.0129 -0.0343 0.0644 -0.00603 0.0231 0.0373 -0.0612 -0.0215 0.00551 0.0555 0.0273 0.0269 0.025 0.0248 0.00859
LP_ServiceFees 0.117 0.096 0.0979 0.000201 -0.124 -0.238 -0.238 -0.0488 -0.0434 -0.0415 -0.0687 -0.108 0.0551 0.0499 0.0962 0.016 -0.0933 0.0594 -0.1 -0.0123 -0.0889 -0.483 -0.462 -0.703 -0.577 -0.863 1 0.0852 -0.0494 -0.0444 -0.0413 0.0696 -0.039 -0.035 -0.0567 -0.525 -0.0403 -0.0507 -0.177 0.00598 0.0444 -0.0341 -0.00484 0.0532 -0.00959 -0.0339 -0.0159 -0.0143 -0.00997 -0.0135 -0.00316
LP_CollectionFees -0.0415 -0.0534 -0.053 0.00995 -0.00106 0.0312 0.0312 0.016 0.0257 -0.00164 0.0273 0.00663 -0.0502 -0.0472 -0.0152 -0.00215 0.00108 0.000646 0.0178 -0.0116 -0.105 -0.0262 -0.0394 -0.0548 -0.0257 -0.134 0.0852 1 -0.216 -0.149 -0.623 0.00619 -0.0159 -0.00832 -0.0101 -0.0745 0.00827 0.226 0.0186 0.000864 -0.112 0.0593 0.113 -0.201 0.00374 -0.0451 -0.0307 -0.0261 -0.0304 -0.00977 -0.00286
LP_GrossPrincipalLoss 0.0673 0.101 0.0995 -0.0362 0.0243 -0.0611 -0.0611 -0.00654 -0.019 -0.00381 -0.0309 0.0162 0.189 0.187 0.0513 0.00396 0.032 -0.0225 -0.00603 0.0724 0.251 0.127 0.182 -0.0266 -0.065 0.116 -0.0494 -0.216 1 0.993 0.26 -0.0131 0.0414 0.0209 0.0288 0.22 -0.0515 -0.654 -0.0863 -0.00194 0.569 -0.208 -0.291 0.365 -0.0125 -0.0248 -0.0142 -0.0166 -0.0154 -0.0152 -0.00348
LP_NetPrincipalLoss 0.0674 0.1 0.0992 -0.0357 0.0247 -0.0607 -0.0607 -0.00588 -0.0178 -0.00369 -0.03 0.0164 0.189 0.187 0.0515 0.00442 0.0324 -0.0217 -0.00574 0.0728 0.247 0.127 0.183 -0.028 -0.0656 0.112 -0.0444 -0.149 0.993 1 0.207 -0.0136 0.0409 0.0203 0.0283 0.216 -0.0516 -0.645 -0.086 -0.00192 0.571 -0.205 -0.287 0.344 -0.0123 -0.0244 -0.014 -0.0163 -0.0152 -0.015 -0.00343
LP_NonPrincipalRecoverypayments 0.0285 0.0421 0.041 -0.00479 0.00532 -0.0416 -0.0416 -0.00201 -0.0109 0.0119 -0.0138 0.00361 0.0631 0.066 0.0204 -0.000823 0.00641 0.0053 -0.0144 0.0112 0.108 0.0243 0.0418 0.0013 -0.0138 0.0526 -0.0413 -0.623 0.26 0.207 1 0.000776 0.0168 0.0101 0.0186 0.067 -0.0099 -0.203 -0.0193 -0.000604 0.139 -0.0646 -0.0906 0.17 -0.00387 -0.0077 -0.0044 -0.00516 -0.00479 -0.00472 -0.00108
PercentFunded -0.0282 -0.029 -0.0292 0.000998 -0.0057 -0.0304 -0.0304 0.00807 0.00698 -0.00178 -0.000285 -0.00524 0.012 0.0194 0.00513 -0.00569 -0.00793 0.0144 -0.00994 0.00232 0.0131 -0.0102 -0.00845 -0.0514 -0.0411 -0.0669 0.0696 0.00619 -0.0131 -0.0136 0.000776 1 0.0096 0.00162 0.00334 -0.0497 -0.0128 0.0113 -0.0149 0.000524 -0.0148 0.00467 0.00446 0.00812 -0.00499 -0.00492 -0.0019 -0.00339 -0.0068 -0.00578 0.000937
Recommendations -0.0442 -0.0306 -0.0311 -0.0394 -0.00958 -0.0351 -0.0351 -0.0113 -0.0211 -0.00731 -0.0115 -0.0215 0.0443 0.0548 0.0225 0.0161 -0.00513 -0.0161 0.0205 0.0331 0.147 -0.0182 -0.00998 0.0808 0.0846 0.0361 -0.039 -0.0159 0.0414 0.0409 0.0168 0.0096 1 0.718 0.322 0.0738 -0.058 -0.0363 -0.0198 -0.000957 0.0386 0.0947 -0.116 0.0178 -0.00489 -0.00842 -0.00533 -0.00583 -0.00708 -0.00491 -0.00171
InvestmentFromFriendsCount -0.0468 -0.0373 -0.0373 -0.0314 -0.0104 -0.0132 -0.0132 -0.0133 -0.0186 -0.0161 -0.00848 -0.0122 0.0236 0.0291 0.0133 0.00714 -0.000434 -0.0219 0.0279 0.0342 0.11 -0.00751 -0.00286 0.0755 0.0808 0.0279 -0.035 -0.00832 0.0209 0.0203 0.0101 0.00162 0.718 1 0.484 0.0588 -0.0431 -0.0135 -0.0178 -0.000669 0.0188 0.0857 -0.0908 0.00447 -0.00161 -0.00762 -0.00252 -0.00571 -0.0053 -0.00522 -0.0012
InvestmentFromFriendsAmount -0.0318 -0.0265 -0.0266 -0.0208 -0.00761 -0.023 -0.023 -0.00903 -0.0101 -0.00684 -0.00523 0.00281 0.0227 0.0234 0.0153 0.00564 0.0129 -0.00456 0.0049 0.0279 0.0653 0.0204 0.0267 0.0748 0.0751 0.0445 -0.0567 -0.0101 0.0288 0.0283 0.0186 0.00334 0.322 0.484 1 0.0122 -0.0256 -0.0124 -0.0127 -0.000372 0.0181 0.0475 -0.0538 4.51e-05 -0.00202 -0.00441 -0.00214 -0.00318 -0.00295 -0.00291 -0.000666
Investors -0.308 -0.274 -0.274 -0.0416 0.125 0.271 0.271 0.0394 0.0252 0.00823 0.0498 0.0555 0.0135 0.0263 -0.0871 -0.027 0.0722 -0.158 0.208 0.00409 0.259 0.38 0.387 0.563 0.551 0.382 -0.525 -0.0745 0.22 0.216 0.067 -0.0497 0.0738 0.0588 0.0122 1 -0.0279 -0.0602 0.0944 -0.00359 0.0519 0.19 -0.224 0.042 -0.00153 -0.011 -0.00326 -0.00976 -0.00614 -0.00181 0.000154
CreditHistory -0.0289 -0.0531 -0.0521 0.32 0.201 0.225 0.225 0.209 0.207 0.368 0.259 0.302 -0.101 -0.102 -0.0222 0.0429 0.214 0.0798 0.155 -0.00016 -0.284 0.166 0.144 -0.0395 -0.0503 0.0129 -0.0403 0.00827 -0.0515 -0.0516 -0.0099 -0.0128 -0.058 -0.0431 -0.0256 -0.0279 1 0.105 0.238 -0.00414 -0.0833 -0.17 0.237 -0.0785 0.00428 0.0154 0.00455 0.012 0.0044 0.0027 0.000167
LoanGoodBad -0.195 -0.235 -0.232 0.062 0.0478 0.267 0.267 0.0748 0.0914 0.0486 0.122 0.0738 -0.248 -0.236 -0.202 -0.0258 0.0266 -0.00698 0.0822 -0.0536 -0.355 0.123 0.0774 0.12 0.151 -0.0343 -0.0507 0.226 -0.654 -0.645 -0.203 0.0113 -0.0363 -0.0135 -0.0124 -0.0602 0.105 1 0.201 -0.0148 -0.765 0.318 0.445 -0.479 0.019 -0.188 -0.108 -0.126 -0.117 -0.115 -0.0264
IncomeRangeCat -0.0861 -0.128 -0.129 0.178 0.253 0.309 0.309 0.254 0.254 0.257 0.241 0.268 -0.122 -0.0777 -0.19 0.0164 0.191 0.0782 0.145 -0.104 -0.452 0.339 0.313 0.0527 0.0433 0.0644 -0.177 0.0186 -0.0863 -0.086 -0.0193 -0.0149 -0.0198 -0.0178 -0.0127 0.0944 0.238 0.201 1 -0.0134 -0.129 -0.148 0.289 -0.182 0.0132 0.0113 0.00695 0.0066 0.00544 0.00192 -0.00102
Cancelled -0.00107 -0.000741 -0.000382 -0.00214 -0.00138 -0.0081 -0.0081 -0.00074 -0.00138 -0.00273 -0.00854 -0.00557 0.00503 0.00112 0.00723 -0.000422 -0.00157 -0.00493 4.48e-05 -0.00139 0.0125 -0.00704 -0.00725 -0.00578 -0.00506 -0.00603 0.00598 0.000864 -0.00194 -0.00192 -0.000604 0.000524 -0.000957 -0.000669 -0.000372 -0.00359 -0.00414 -0.0148 -0.0134 1 -0.00227 -0.00469 -0.00658 -0.00142 -0.000281 -0.000559 -0.00032 -0.000375 -0.000348 -0.000343 -7.85e-05
Chargedoff 0.166 0.195 0.193 -0.0561 -0.0411 -0.189 -0.189 -0.0899 -0.102 -0.0546 -0.103 -0.0688 0.187 0.178 0.137 0.0237 -0.0364 -0.0102 -0.0726 0.0386 0.273 -0.106 -0.0661 -0.0927 -0.116 0.0231 0.0444 -0.112 0.569 0.571 0.139 -0.0148 0.0386 0.0188 0.0181 0.0519 -0.0833 -0.765 -0.129 -0.00227 1 -0.243 -0.341 -0.0736 -0.0146 -0.0289 -0.0166 -0.0194 -0.018 -0.0177 -0.00406
Completed -0.0885 -0.06 -0.058 -0.106 -0.0345 0.000206 0.000206 -0.0778 -0.11 -0.0707 -0.103 -0.111 0.0573 0.0574 0.00134 -0.00845 -0.0383 -0.0976 0.0403 -0.0152 0.511 -0.244 -0.197 0.464 0.536 0.0373 -0.0341 0.0593 -0.208 -0.205 -0.0646 0.00467 0.0947 0.0857 0.0475 0.19 -0.17 0.318 -0.148 -0.00469 -0.243 1 -0.704 -0.152 -0.0301 -0.0598 -0.0342 -0.0401 -0.0372 -0.0366 -0.0084
Current -0.0629 -0.119 -0.119 0.143 0.068 0.198 0.198 0.126 0.167 0.103 0.189 0.16 -0.238 -0.229 -0.151 -0.0107 0.0543 0.0859 0.0212 -0.0251 -0.745 0.322 0.244 -0.349 -0.393 -0.0612 -0.00484 0.113 -0.291 -0.287 -0.0906 0.00446 -0.116 -0.0908 -0.0538 -0.224 0.237 0.445 0.289 -0.00658 -0.341 -0.704 1 -0.213 -0.0422 -0.0838 -0.048 -0.0561 -0.0521 -0.0514 -0.0118
Defaulted 0.0537 0.087 0.0847 -0.0314 -0.022 -0.207 -0.207 0.0107 -0.00329 -0.00983 -0.0614 -0.0257 0.183 0.18 0.165 0.00556 0.0162 0.0292 -0.03 0.0368 0.283 -0.0636 -0.0434 -0.0706 -0.0768 -0.0215 0.0532 -0.201 0.365 0.344 0.17 0.00812 0.0178 0.00447 4.51e-05 0.042 -0.0785 -0.479 -0.182 -0.00142 -0.0736 -0.152 -0.213 1 -0.00911 -0.0181 -0.0104 -0.0121 -0.0113 -0.0111 -0.00254
FinalPaymentInProgress 0.00567 0.00238 0.00243 0.000688 0.00273 0.00949 0.00949 -0.00059 0.000541 0.00214 0.003 0.00137 -0.00556 -0.00352 -0.00455 0.000759 0.00467 4.84e-05 0.00143 -0.00375 -0.0232 6.19e-05 0.00573 0.008 0.00782 0.00551 -0.00959 0.00374 -0.0125 -0.0123 -0.00387 -0.00499 -0.00489 -0.00161 -0.00202 -0.00153 0.00428 0.019 0.0132 -0.000281 -0.0146 -0.0301 -0.0422 -0.00911 1 -0.00358 -0.00205 -0.0024 -0.00223 -0.0022 -0.000503
Past Due (1-15 days) 0.0473 0.0429 0.0432 0.00387 -0.0062 0.00255 0.00255 -0.00633 -0.00867 0.00511 -0.00836 -0.00593 -0.0106 -0.0135 -0.00436 0.000957 -0.00653 0.00317 -0.0163 -0.00171 -0.0442 0.00178 0.00556 -0.00293 -0.0196 0.0555 -0.0339 -0.0451 -0.0248 -0.0244 -0.0077 -0.00492 -0.00842 -0.00762 -0.00441 -0.011 0.0154 -0.188 0.0113 -0.000559 -0.0289 -0.0598 -0.0838 -0.0181 -0.00358 1 -0.00408 -0.00477 -0.00443 -0.00437 -0.001
Past Due (16-30 days) 0.0298 0.0274 0.0276 -0.00256 -0.00207 -0.00246 -0.00246 0.00204 0.00304 0.00615 0.00248 0.00326 -0.00595 -0.00703 0.000841 0.00398 0.00243 0.00252 -0.00452 0.0055 -0.0253 -0.0014 0.00112 -0.00443 -0.0132 0.0273 -0.0159 -0.0307 -0.0142 -0.014 -0.0044 -0.0019 -0.00533 -0.00252 -0.00214 -0.00326 0.00455 -0.108 0.00695 -0.00032 -0.0166 -0.0342 -0.048 -0.0104 -0.00205 -0.00408 1 -0.00273 -0.00253 -0.0025 -0.000572
Past Due (31-60 days) 0.033 0.0304 0.0306 -0.00159 0.000273 0.00503 0.00503 -0.0081 -0.00663 0.00252 -0.00626 -0.00581 -0.00391 -0.00648 -0.000941 0.0103 -0.00568 -0.000307 -0.0018 -0.00205 -0.0303 0.00179 0.00267 -0.00845 -0.0178 0.0269 -0.0143 -0.0261 -0.0166 -0.0163 -0.00516 -0.00339 -0.00583 -0.00571 -0.00318 -0.00976 0.012 -0.126 0.0066 -0.000375 -0.0194 -0.0401 -0.0561 -0.0121 -0.0024 -0.00477 -0.00273 1 -0.00297 -0.00292 -0.00067
Past Due (61-90 days) 0.0356 0.0331 0.0333 0.00336 0.00708 0.00252 0.00252 -0.00736 -0.00548 -0.000643 -0.00436 -0.0037 -0.00574 -0.00888 0.00218 0.00669 -0.00401 0.00292 -0.00703 0.0093 -0.0267 -0.0051 -0.00393 -0.00942 -0.0183 0.025 -0.00997 -0.0304 -0.0154 -0.0152 -0.00479 -0.0068 -0.00708 -0.0053 -0.00295 -0.00614 0.0044 -0.117 0.00544 -0.000348 -0.018 -0.0372 -0.0521 -0.0113 -0.00223 -0.00443 -0.00253 -0.00297 1 -0.00271 -0.000622
Past Due (91-120 days) 0.0335 0.0315 0.0316 -0.0044 -0.00897 0.00361 0.00361 -0.00495 -0.00442 -0.00234 -0.00139 -0.00348 -0.00737 -0.00866 -0.0044 -0.00191 -0.00679 0.000605 -0.00427 0.00148 -0.0243 -0.00276 -0.00243 -0.00778 -0.0164 0.0248 -0.0135 -0.00977 -0.0152 -0.015 -0.00472 -0.00578 -0.00491 -0.00522 -0.00291 -0.00181 0.0027 -0.115 0.00192 -0.000343 -0.0177 -0.0366 -0.0514 -0.0111 -0.0022 -0.00437 -0.0025 -0.00292 -0.00271 1 -0.000613
Past Due (>120 days) 0.00961 0.0095 0.00954 0.00398 -0.000106 0.000345 0.000345 -0.00422 -0.00415 -0.0042 -0.00728 -0.00631 -0.000598 -0.00375 -0.000929 0.00101 -0.00393 -0.0048 -0.0042 3.99e-05 -0.00517 -0.000106 0.000552 -0.00101 -0.00368 0.00859 -0.00316 -0.00286 -0.00348 -0.00343 -0.00108 0.000937 -0.00171 -0.0012 -0.000666 0.000154 0.000167 -0.0264 -0.00102 -7.85e-05 -0.00406 -0.0084 -0.0118 -0.00254 -0.000503 -0.001 -0.000572 -0.00067 -0.000622 -0.000613 1
In [61]:
plt.figure(figsize=[80, 80])
sb.heatmap(corr, annot=True, fmt='f', linewidths=.5);

Correlation between all numeric variables

In [63]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="BorrowerRate", y="CreditScoreRangeUpper", hue='LoanGoodBad', size='LoanOriginalAmount', alpha=0.6, data=df)
plt.ylim(350, 950);
In [67]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="BorrowerRate", y="CreditScoreRangeUpper", hue='LoanGoodBad', size='IncomeRangeCat', alpha=0.6, data=df)
plt.ylim(350, 950);
In [69]:
plt.figure(figsize=[20, 10])
sb.regplot(x="BorrowerRate", y="CreditScoreRangeUpper", scatter_kws={'alpha': 1/8}, data=df)
plt.ylim(350, 950);
/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
  1. Credit Score is one factor in Loan outcome. Higher the score higher possible it being a good loan.
  2. Interest Rate is another factor. Lower the interest rate higher possible it being a good loan.
In [71]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="BorrowerRate", y="BankcardUtilization", hue='LoanGoodBad', size='LoanOriginalAmount', alpha=0.4, data=df)
plt.ylim(0, 2);
In [73]:
plt.figure(figsize=[20, 10])
sb.regplot(x="BorrowerRate", y="BankcardUtilization", scatter_kws={'alpha': 1/8}, data=df)
plt.ylim(0, 2.5);
  1. More one utilize their bank credit higher their loan interest rate goes.
In [75]:
plt.figure(figsize=[20, 10])
markers = {0: "s", 1: "X"}
sb.scatterplot(x="BorrowerRate", y="LoanOriginalAmount", style='LoanGoodBad', markers=markers, alpha=1/8, data=df);
plt.xlim(0.0, 0.4);
In [77]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="BorrowerRate", y="LoanOriginalAmount", hue='LoanGoodBad', size='IncomeRangeCat', alpha=1/8, data=df)
plt.xlim(0.0, 0.4);
In [78]:
plt.figure(figsize=[20, 10])
sb.regplot(x="BorrowerRate", y='LoanOriginalAmount', scatter_kws={'alpha': 1/8}, data=df);
  1. Higher loan get lower Interest Rate.
In [80]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="BorrowerRate", y='Investors', hue='LoanGoodBad', size='LoanOriginalAmount', alpha=1/8, data=df)
plt.xlim(0.0, 0.4)
plt.ylim(0, 1000);
In [82]:
plt.figure(figsize=[20, 10])
sb.regplot(x="BorrowerRate", y='Investors', scatter_kws={'alpha': 1/8}, data=df);
plt.xlim(0.0, 0.4)
plt.ylim(0, 1000);
  1. Lower interest rate loans get more Investers.
In [86]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="CreditScoreRangeUpper", y='LoanOriginalAmount', hue='LoanGoodBad', alpha=1/8, data=df)
plt.xlim(350, 950);
In [85]:
plt.figure(figsize=[20, 10])
sb.regplot(x='CreditScoreRangeUpper', y='LoanOriginalAmount', scatter_kws={'alpha': 1/8}, data=df);
plt.xlim(350, 950)
plt.ylim(0, 40000);
  1. Good loans are associated with higher credit score.
  2. Higher the Credit Score higher the loan amount given.
In [88]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="CreditScoreRangeUpper", y='BankcardUtilization', hue='LoanGoodBad', size='LoanOriginalAmount', alpha=1/8, data=df);
plt.ylim(0, 3);
In [89]:
plt.figure(figsize=[20, 10])
sb.regplot(x='CreditScoreRangeUpper', y='BankcardUtilization', scatter_kws={'alpha': 1/8}, data=df)
plt.ylim(0, 2.5);

One having higher Bank credit utilization tends to result in lower credit score.

In [91]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x="CreditScoreRangeUpper", y='Investors', hue='LoanGoodBad', size='LoanOriginalAmount', alpha=1/8, data=df);
plt.xlim(350, 950);
In [92]:
plt.figure(figsize=[20, 10])
sb.regplot(x='CreditScoreRangeUpper', y='Investors', scatter_kws={'alpha': 1/8}, data=df)
plt.ylim(0, 1200)
plt.xlim(400, 950);

Higher Credit Score also one factor Investors interested in loan.

In [94]:
plt.figure(figsize=[20, 10])
sb.scatterplot(x='LoanOriginalAmount', y='Investors', hue='LoanGoodBad', alpha=1/8, data=df);
In [96]:
plt.figure(figsize=[20, 10])
sb.regplot(x='LoanOriginalAmount', y='Investors', scatter_kws={'alpha': 1/8}, data=df);

Higher loan amount more Investors to invest.

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

  1. Credit Score is one factor in Loan outcome. Higher the score higher possible it being a good loan.
  2. Interest Rate is another factor. Lower the interest rate higher possible it being a good loan amount.
  3. More one utilize their bank credit higher their loan interest rate goes.
  4. Higher loan get lower Interest Rate.
  5. Good loans are associated with higher credit score.
  6. Higher the Credit Score higher the loan amount given.
  7. Higher loan amount more Investors to invest.

Were there any interesting or surprising interactions between features?

  1. Lower interest rate loans get more Investers.
  2. One having higher Bank credit utilization tends to result in lower credit score.
  3. Higher Credit Score also one factor Investors interested in loan.

At the end of your report, make sure that you export the notebook as an html file from the File > Download as... > HTML menu. Make sure you keep track of where the exported file goes, so you can put it in the same folder as this notebook for project submission. Also, make sure you remove all of the quote-formatted guide notes like this one before you finish your report!